code: def plot_roc(
      roc_data: Dict[str, Dict[str, np.ndarray]],
      figsize: tuple = (10, 5),
  ) -> None:
      """
      Plot ROC curve.
  
      Parameters
      ----------
      roc_data
          Dictionary with as key the label to show in the legend and as value another dictionary with as
          keys `scores` and `labels` with respectively the outlier scores and outlier labels.
      figsize
          Figure size.
      """
      fig, axes = plt.subplots(nrows=1, ncols=1, figsize=figsize)
  
      for k, v in roc_data.items():
          fpr, tpr, thresholds = roc_curve(v["labels"], v["scores"])
          roc_auc = auc(fpr, tpr)
          plt.plot(
              fpr, tpr, lw=1, label="{}: AUC={:.4f}".format(k, roc_auc)
          )
  
      plt.plot([0, 1], [0, 1], color="black", lw=1, linestyle="--")
      plt.xlim([0.0, 1.0])
      plt.ylim([0.0, 1.05])
      plt.xlabel("False Positive Rate")
      plt.ylabel("True Positive Rate")
      plt.title("{}".format("ROC curve"))
      plt.legend(loc="lower right", ncol=1)
      plt.grid()
      plt.show()
  
file_path: alibi-detect/alibi_detect/utils/visualize.py
imports: import matplotlib.pyplot
  import numpy
  import pandas
  from sklearn.metrics import roc_curve, auc
  from typing import Dict, Union
  import warnings
label_desc: Use image node_id 5 for calling a global function with example usage: plot_roc(roc_data, figsize) without return types